import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt

import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib

For this excercise, we have written the following code to load the stock dataset built into plotly express.

stocks = px.data.stocks()
stocks.head()

	date	GOOG	AAPL	AMZN	FB	NFLX	MSFT
0	2018-01-01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	2018-01-08	1.018172	1.011943	1.061881	0.959968	1.053526	1.015988
2	2018-01-15	1.032008	1.019771	1.053240	0.970243	1.049860	1.020524
3	2018-01-22	1.066783	0.980057	1.140676	1.016858	1.307681	1.066561
4	2018-01-29	1.008773	0.917143	1.163374	1.018357	1.273537	1.040708

Question 1:

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

x = stocks["date"]
y = stocks["GOOG"]

fig, ax = plt.subplots(figsize = (20,8))
ax.plot(x,y, marker = 'o') #marker= rondje op ieder punt
ax.set_xlabel('date')
ax.set_ylabel('GOOG')
ax.set_title('Stocks information')
ax.set_xticks([0, 25, 50,75, 100])
plt.grid()
plt.show()

Question 2:

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

x = stocks["date"]
yGOOG = stocks["GOOG"]
yAAPL = stocks["AAPL"]
yAMZN = stocks["AMZN"]
yFB = stocks["FB"]
yNFLX = stocks["NFLX"]
yMSFT = stocks["MSFT"]

fig, ax = plt.subplots(figsize = (20,8))
ax.plot(x, yGOOG, marker = 'o')
ax.plot(x, yAAPL, marker = 'o')
ax.plot(x, yAMZN, marker = 'o')
ax.plot(x, yFB, marker = 'o')
ax.plot(x, yNFLX, marker = 'o')
ax.plot(x, yMSFT, marker = 'o')
#ax.legend('GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT') does not work
ax.set_xlabel('date')
ax.set_ylabel('Different Stocks')
ax.set_title('Stocks information')
ax.set_xticks([0 , 25, 50,75, 100])
plt.grid()
plt.show()

Seaborn

First, load the tips dataset

tips = sns.load_dataset('tips')
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Question 3:

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

• Are there differences between male and female when it comes to giving tips?
• What attribute correlate the most with tip?

tips = sns.load_dataset('tips')
tips.head()

g= sns.FacetGrid(tips, col = 'sex', hue = 'smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.savefig('smoker.png', dpi=200)
plt.show()

d= sns.FacetGrid(tips, col = 'sex', hue = 'day')
d.map(sns.scatterplot, 'total_bill', 'tip')
d.add_legend()
plt.savefig('smoker.png', dpi=200)
plt.show()

t= sns.FacetGrid(tips, col = 'sex', hue = 'time')
t.map(sns.scatterplot, 'total_bill', 'tip')
t.add_legend()
plt.savefig('smoker.png', dpi=200)
plt.show()

s= sns.FacetGrid(tips, col = 'sex', hue = 'size')
s.map(sns.scatterplot, 'total_bill', 'tip')
s.add_legend()
plt.savefig('smoker.png', dpi=200)
plt.show()

Plotly Express

Question 4:

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset

Hints:

• Turn stocks dataframe into a structure that can be picked up easily with plotly express

fig = px.line(stocks, x="date", y="GOOG", title= "Stock information")
fig.show()

fig = px.line(stocks, x="date", y=["GOOG", "AAPL", "AMZN", "FB", "NFLX", "MSFT"], title= "Stock information")
fig.show()

The tips dataset

tips = sns.load_dataset('tips')
tips.head()

import plotly.express as px
df = px.data.tips()

fig = px.box(df, x="sex", y="total_bill", color="smoker")
fig.show()

fig = px.box(df, x="sex", y="total_bill", color="day")
fig.show()

fig = px.box(df, x="sex", y="total_bill", color="time")
fig.show()

fig = px.box(df, x="sex", y="total_bill", color="size")
fig.show()

Question 5:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

• Extract the 2007 year data from the dataframe. You have to process the data accordingly
• use plotly bar
• Add different colors for different continents
• Sort the order of the continent for the visualisation. Use axis layout setting
• Add text to each bar that represents the population

df = px.data.gapminder()
df.head()

#load data
df = px.data.gapminder()
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new = df_2007_new.sort_values('pop', ascending=False)
fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index, orientation='h', color = df_2007_new.index)
fig.show()